https://github.com/Andrew-Qibin/CoordAttention
Rethinking Bottleneck Structure for Efficient Mobile Network Design
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Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Rethinking Bottleneck Structure for Efficient Mobile Network Design
Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation
Practical Bayesian Optimization of Machine Learning Algorithms
MoViNets: Mobile Video Networks for Efficient Video Recognition
Bilinear Scoring Function Search for Knowledge Graph Learning
Bilinear Scoring Function Search for Knowledge Graph Learning
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
Optuna: A Next-generation Hyperparameter Optimization Framework
Optimizing Millions of Hyperparameters by Implicit Differentiation
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Scalable Factorized Hierarchical Variational Autoencoder Training
Single Path One-Shot Neural Architecture Search with Uniform Sampling
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.